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For experimental purposes, I train the SVM model as follows,

clf = SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',
max_iter=-1, probability=False, random_state=None, shrinking=True,
tol=0.001, verbose=True)
scores = cross_val_score(clf,train_feature,train_label,cv=3)
print(scores)

The printed result looks like as follows

Warning: using -h 0 may be faster

optimization finished, #iter = 2182
obj = -794.208203, rho = 1.303717
nSV = 1401, nBSV = 992
Total nSV = 1401

The cross-validation score is like 

[LibSVM][LibSVM][LibSVM][ 0.68838493  0.6887449   0.75864138]

I think nSV represents the number of support vectors. Is that right? Then what do nBSV and rho represent? How can I know whether these cross-validations score are a good indicator for the model performance?

user297850
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    You might find this useful: [LIBSVM FAQ](http://www.csie.ntu.edu.tw/~cjlin/libsvm/faq.html). Perform a search in that doc for `nBSV` and `rho` and you'll probably find what you're looking for. – blacksite Feb 02 '17 at 02:45
  • Check [this question](http://stackoverflow.com/questions/41861676/verbose-log-abbriviations-meaning-in-svc-scikit-learn/41863618#41863618) where i did exactly what not_a_robot proposed! the question about CV-score meaning is a bit broad. – sascha Feb 02 '17 at 17:34
  • Possible duplicate of [Verbose log abbriviations meaning in SVC, scikit-learn](http://stackoverflow.com/questions/41861676/verbose-log-abbriviations-meaning-in-svc-scikit-learn) – talonmies Mar 12 '17 at 04:49

0 Answers0